“I
was amazed,” he comments. “It’s a wonderful conclusion to my past five years of
work.” The conference, which provides a peer-reviewed venue for cutting-edge
research in the field, recognized Matteo for a poster on his doctoral
dissertation (“Sampling-based randomized algorithms for big data analytics”),
which stood out among forty-four other competing entries.

Matteo places his research in the context of what he calls
one of the biggest challenges in contemporary computer science: effective usage
of extremely large amounts of data. In other words, what portion of a dataset
do we need to analyze in order to draw worthwhile conclusions? “Through sampling
of random small portions of the data,” he explains, “we can detect what’s
interesting about the dataset as a whole by only analyzing a much smaller
subset. My research uses very recently-developed probabilistic tools that were
considered to be highly theoretical, but I proved that they can be applied
successfully in practice.”

Professor Eli Upfal,
Riondato’s advisor, sees this practical application of sophisticated theory as
one of his student’s hallmarks. “Matteo has a very strong background in math
but is also an excellent systems programmer. He has a unique ability to apply
theory and find practical uses that are almost limitless.” Matteo’s other work
on frequent item set mining and association rules, Eli explains, has
implications for computational biology, and his research into betweenness
centrality allows analysis that can help determine important actors in massive
social networks such as Facebook and Twitter.

“I love that there are so many possibilities for my research,”
Riondato concludes. “It made me really happy to have people come up to me at
the conference and ask questions, to realize that they might find some
inspiration in my work. It makes me feel like part of a community.” As database
management and data analysis continue to grow in worldwide importance in the
years ahead, that community is surely looking forward to Matteo’s next
contributions to the field.